| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,196 |
| تعداد مقالات | 35,423 |
| تعداد مشاهده مقاله | 52,243,751 |
| تعداد دریافت فایل اصل مقاله | 91,777,155 |
شناسایی کانونهای فعال گردوغبار با روشهای میدانی و دورسنجی بهمنظور تعیین سرعت آستانه فرسایش بادی (مطالعه موردی: شرق استان کرمان) | ||
| تحقیقات حمایت و حفاظت جنگلها و مراتع ایران | ||
| مقاله 4، دوره 21، شماره 1 - شماره پیاپی 41، شهریور 1402، صفحه 55-75 اصل مقاله (1 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfrpr.2023.361243.1565 | ||
| نویسندگان | ||
| رسول خوارزمی* 1؛ حمیدرضا عباسی2؛ سارا مرادی ثانی3؛ فرهاد خاکساریان4 | ||
| 1استادیار، بخش تحقیقات شناسایی خاک و ارزیابی اراضی، مؤسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران | ||
| 2استادیار، بخش تحقیقات بیابان، مؤسسه تحقیقات جنگلها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران | ||
| 3دانشجوی کارشناسی ارشد خاکشناسی، گروه مهندسی و علوم خاک، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 4محقق، گروه تحقیقات تپههای شنی، مؤسسه تحقیقات جنگلها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران | ||
| چکیده | ||
| این پژوهش با هدف شناسایی کانونهای گردوغبار و حساسیت خاک به بادبردگی در شرق استان کرمان با تلفیقی از روشهای میدانی و سنجش از دور انجام شد. درمجموع ده محدوده در منطقه مورد مطالعه با مساحت 3007 کیلومترمربع بر مبنای دورسنجی و بررسی میدانی در زمستان 1399 شناسایی شد. مناطق مستعد تولید گردوغبار منطقه و تعیین تناوب رخداد گردوغبار در 20 سال گذشته با شاخص عمق اپتیکی حاصل از سنجنده مودیس ترا و آکوا تعیین شد. برای بررسی میزان فرسایشپذیری خاکها، سرعت آستانه فرسایش بادی و میزان بادبردگی در سه کلاس سرعت 15، 20 و 25 متر بر ثانیه با 17 نمونه خاک در تونل باد اندازهگیری شد. مقادیر قابلیت هدایت الکتریکی (EC) خاک نیز با 22 نمونه در آزمایشگاه تعیین و بعد نقشه شوری خاک با روش کریجینگ تهیه شد. نتایج حاصل از شاخص پوشش گیاهی تعمیم یافت و رابطه خطی EC در محیط گوگلارث انجین محاسبه و با نتایج کریجینگ مقایسه گردید که بیانگر شوری شدید کانونهای مورد بررسی است (EC>16). نتایج تحلیل بادهای فرساینده و رژیم باد غالب بر مبنای دادههای پنج ایستگاه هواشناسی در منطقه نشان داد، ایستگاههای ریگان و نصرتآباد بهترتیب با 561 و 1199 قابلیت حمل ماسه برحسب واحد برداری و بادهای با سرعت 40 نات (57/20 متربرثانیه) جزو مناطق با انرژی باد زیاد دستهبندی میشوند. نتایج تعیین فرسایشپذیری خاکها نشان داد، بخشهای جنوبی دارای کمترین میزان سرعت آستانه فرسایش بادی (بین شش تا هشت متر بر ثانیه) هستند که جزو مناطق حساس به فرسایش بادی گروهبندی شدند و میزان تجمعی بادبردگی آنها در سه کلاس سرعت باد، 60 کیلوگرم بر مترمربع در دقیقه است. با توجه به نتایج، مناطق مورد مطالعه از کانونهای داخلی مهم گردوغبار محسوب میشوند که نیازمند اقدامات مدیریتی برای کنترل کانونهای برداشت است. | ||
| کلیدواژهها | ||
| تونل باد؛ شاخص AOD؛ شوری خاک؛ گردوغبار؛ گوگل ارث انجین | ||
| عنوان مقاله [English] | ||
| Identification of Active Dust Source Areas Using Field and Remote Sensing Methods for Determining Wind Erosion Threshold Velocity (Case Study: Eastern Kerman Province) | ||
| نویسندگان [English] | ||
| Rasoul Kharazmi1؛ Hamidreza Abbasi2؛ Sara Moradi sani3؛ Farhad Khaksarian4 | ||
| 1Assistant Prof., Research Division of Soil Identification and Land Valuation, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. | ||
| 2Assistant Prof., Research Division of Desert, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran | ||
| 3MSc Student of Soil Science, Department of Soil Sciences, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran | ||
| 4Researcher, Research Division of Sand Dunes, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran | ||
| چکیده [English] | ||
| This study was conducted in 2020 with the objective of identifying dust source areas and soil susceptibility to wind erosion in the eastern part of Kerman Province, through the integration of field and remote sensing methods. In total, ten study areas covering 3007 square kilometers were identified based on remote sensing and field surveys during the winter of 2020. The dust-prone areas and the determination of the dust event frequency in the past 20 years were established using the optical depth index derived from MODIS Terra and Aqua satellites. To assess soil erodibility, wind erosion threshold velocity, and wind erosion rates, soil samples (17 in total) were measured in a wind tunnel at three wind speed classes: 15, 20, and 25 meters per second. Electrical conductivity (EC) values of soil samples (22 in total) were determined in the laboratory to create a soil salinity map using the kriging method. The results of the vegetation cover index were extended, and the linear relationship between EC was computed using Google Earth Engine and compared with kriging results, indicating significant salinity in the studied areas (EC>16). The analysis of prevailing wind directions and wind regimes based on data from five meteorological stations in the region demonstrated that the Rigān and Nasratābād stations, with transport potential of 561 and 1199 kg m^-1 s^-1, respectively, were classified as high-energy wind zones. Wind speeds of 40 knots (20.57 m/s) were prevalent in these areas. The assessment of soil erodibility revealed that the southern parts had the lowest wind erosion threshold velocity (between 6 to 8 m/s), classifying them as wind erosion-sensitive zones, with an accumulated wind erosion rate of 60 kg/m^2/year across the three wind speed classes. Based on the findings, the study areas are considered significant internal dust source areas, requiring management actions to control dust emissions. | ||
| کلیدواژهها [English] | ||
| AOD Index, Dust, Google Earth Engine, Soil Salinity, Wind Tunnel | ||
| مراجع | ||
|
Middle East: A Review. Journal of Environmental Health, 84(3): 8-15.
-Araghi zade, M. and Masoodian, S., 2021. Climate analysis and study of dust storms in Khorasan Razavi. Physical Geography Research Quarterly, 53(3): 305-318 (In Persian).
-Arami, S.A., Ownegh, M., MohammadianBehbahani, A., Akbari, M. and Zarasvandi, A., 2018. The analysis of dust hazard studies in southwest region of Iran in 22 years (1996-2017). Journal of Spatial Analysis Environmental Hazarts, 5(1): 39-66 (In Persian).
-Arjmand, M., Rashki, A. and Sargazi, H., 2018. Monitoring of spatial and temporal variability of desert dust over the Hamoun e Jazmurian, Southeast of Iran based on the Satellite Data. Scientific- Research Quarterly of Geographical Data (SEPEHR), 27(106): 153-168 (In Persian).
-Baddock, M.C., Bullard, J.E. and Bryant, R.G., 2009. Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia. Remote Sensing of Environment, 113(7): 1511-1528.
-Da Silva, D.C., Lopes, P.M.O., da Silva, M.V., de Albuquerque Moura, G.B., Nascimento, C.R., Brito, J.I.B., e Silva, Ê.F.D.F., Rolim, M.M. and de Lima, R.P., 2021. Principal component analysis and biophysical parameters in the assessment of soil salinity in the irrigated perimeter of Bahia, Brazil. Journal of South American Earth Sciences, 112: 103580.
-Dundar, C., Isik, A.G. and Oguz, K., 2019. Temporal analysis of sand and dust storms (SDS) between the years 2003 and 2017 in the Central Asia. In: E3S Web of Conferences, 99: 02004, EDP Sciences.
-Fryberger, S.G. and Dean, G., 1979. Dune forms and wind regime. In A study of global sand seas, 1052: 137-169.
-Ghohroudi tali, M., 2003. A survey of interpolation through the Kriging method. Geographical Research Quarterly, 34(43): 95-108 (In Persian).
-Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A. and Hasanlou, M., 2020. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing, 167:276-288.
-Gillies, J.A., Nickling, W.G., Nickolich, G. and Etyemezian, V.A., 2017. Wind tunnel study of the aerodynamic and sand trapping properties of porous mesh 3-dimensional roughness elements. Aeolian Research, 25: 23-35.
-Giuffrida, F., Carla, C., Angelo, M. and Cherubino, L., 2016. Effects of salt stress imposed during two growth phases on cauliflower production and quality. Journal of the Science of Food and Agriculture, 97: 1552–1560.
-Godie, A.S. and Middleton, N.J., 2006. Desert dust in the global system. Springer Science and Business Media,84-87 p.
-Head office of Natural Resources and Watershed Management of Kerman, 2015. Dust control plan in the east of Kerman province, Rigan, Fahraj, Narmashir and Bam cities, 31p (In Persian).
-Jafari, R. and Sanati, H., 2021. Identification of dust sources and storms in the south of Kerman province using Remote sensing data. Journal of Water and Soil Science, 25 (3): 145-158 (In Persian).
-Kandakji, T., Gill, T.E. and Lee, J.A., 2020. Identifying and characterizing dust point sources in the southwestern United States using Remote sensing and GIS. Geomorphology, 353: 107019.
-Karegar, E., Bodagh Jamali, J., Ranjbar Saadatabadi, A., Moeinaddini, M. and Goshtasb, H., 2017. Simulation and numerical analysis of severe dust storms in the east of Iran. Journal of Spatial Analysis Environmental Hazarts, 3(4): 101-119 (In Persian).
-Khaledi, K., 2016. Estimating the economic losses of dust storms on agriculture sector in the western provinces of the Iran. Agricaltural Economics and Development, 24(4): 151-183 (In Persian).
-Khaleghi, F., Soleimani, GH. and Shojaei, S., 2016. Dust, a new challenge, an unpleasant and uninvited guest in 14 provinces of the Iran, The Head Office of the Kerman Province Environmental, from https://kerman.doe.ir/portal/file/?542460/%D8%B1%D9%8A%D8%B2%DA%AF%D8%B1%D8%AF.pdf (In Persian).
-Kharazmi, R., Panidi, E. A. and Chaban, L.N., 2017. Assessment of arid ecosystems dynamics based on the results of automated processing of multispectral satellite imagery time series. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 14(3): 196-205 (In Russian).
-Kharazmi, R., Panidi, E.A. and Karkon, V.M., 2016. Assessment of dry land ecosystem dynamics based on time series of satellite images. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 13(5): 214-223 (In Russian).
-Kharazmi, R., Tavili, A., Rahdari, M.R., Chaban, L., Panidi, E. and Rodrigo-Comino, J., 2018. Monitoring and assessment of seasonal land cover changes using remote sensing: A 30-year (1987–2016) case study of Hamoun Wetland, Iran. Environmental monitoring and assessment, 190(6): 1-23.
-Kolios, S. and Hatzianastassiou, N., 2019. Quantitative aerosol optical depth detection during dust outbreaks from METEOSAT imagery using an artificial neural network model. Remote Sensing, 11(9): 1022.
-Li, J., He, Q., Ge, X., Abbas, A. and Jin, L., 2021. Spatio-temporal changes of AOD in Xinjiang of China from 2000 to 2019: Which factor is more influential, natural factor or human factor? Plos one, 16(8): e0253942
-Miri, A., Maleki, S. and Middleton, N., 2021. An investigation into climatic and terrestrial drivers of dust storms in the Sistan region of Iran in the early twenty-first century. Science of the Total Environment, 757:143952.
-Nabiollahi, K., Moradian, S. and Taghizadeh-Mehrjardi, R., 2017. Mapping of soil salinity and sodicity hazard using indicator Krijing in Ghorveh, Kurdistan province. Iranian Journal of Soil Research, 31(1): 103-115 (In Persian).
-Namdari, S., Valizade, K.K., Rasuly, A.A. and Sarraf, B.S., 2016. Spatio-temporal analysis of MODIS AOD over western part of Iran.Arabian Journal of Geosciences, 9(3): 1-11.
-Naserpour, S., Shahriari, A. and Zeaiean, P., 2015. Sources of dust storms in south west Iran using satellite images and weather maps. Physical Geography Research Quarterly, 47(1): 4-5 (In Persian).
-Pan, L., Han, Y., Lu, Z., Li, J., Gao, F., Liu, Z., Liu, W. and Liu, Y., 2021. Integrative investigation of dust emissions by dust storms and dust devils in North Africa. Science of the Total Environment, 756: 144128.
-Panayiotopoulos, K.P., Barbayiannis, N. and Papatolios, K., 2004. Influence of electrolyte concentration, sodium adsorption ratio, and mechanical disturbance on dispersed clay particle size and critical flocculation concentration in Alfisols. Communications in Soil Science and Plant Analysis, 35: 1415-1434.
-Rashki, A., Arjmand, M. and Kaskaoutis, D.G., 2017. Assessment of dust activity and dust-plume pathways over Jazmurian Basin, southeast Iran. Aeolian Research, 24: 145-160.
-Rashki, A., Kaskaoutis, D.G., Francois, P., Kosmopoulos, P.G. and Legrand, M.J.A.R., 2015. Dust-storm dynamics over Sistan region, Iran: Seasonality, transport characteristics and affected areas. Aeolian Research, 16:35-48.
-Rashki, A., Kaskaoutis, D.G., Goudie, A.S. and Kahn, R.A., 2013. Dryness of ephemeral lakes and consequences for dust activity: the case of the Hamoun drainage basin, southeastern Iran. Science of the Total Environment, 463: 552-564.
-Rashki, A., Middleton, N.J. and Goudie, A.S., 2021. Dust storms in Iran–Distribution, causes, frequencies and impacts. Aeolian Research, 48: 100655.
-Rayegani, B., kheirandish, Z., Kermani, F., Mohammdi Miyab, M. and Torabinia, A., 2017. Identification of active dust sources using Remote sensing data and air flow simulation (Case study: Alborz province). Desert Management, 4(8): 15-26 (In Persian).
-Refahi, H., 2018. Wind erosion and its control (6th ed.). Tehran University Press, Tehran, 121-138 (In Persian).
-Rezazadeh, M., Irannejad, P. and Shao, Y., 2013. Climatology of the Middle East dust events. Aeolian Research, 10:103-109.
-Salehpourjam, A. and Tabatabaei, M., 2018. Investigation of the soil characteristics on desertification potential in alluvial fans using AHP-FUZZY SAW method (Case study: alluvial fans surrounding Halghe Dareh and Jaroo mountains, Eshtehard). Journal of Natural Environmental Hazards, 7(17): 195-212 (In Persian).
-Shahabinejad, N., Mahmoodabadi, M., Jalalian, A. and Chavoshi, E., 2019. In situ field measurement of wind erosion and threshold velocity in relation to soil properties in arid and semiarid environments. Environmental Earth Sciences, 78(16): 1-22.
-Wu, W., Mhaimeed, A.S., Al-Shafie, W.M., Ziadat, F., Dhehibi, B., Nangia, V. and De Pauw, E., 2014. Mapping soil salinity changes using remote sensing in Central Iraq. Geoderma Regional, 2: 21-31.
-Wu, W., Zucca, C., Muhaimeed, A.S., Al Shafie, W.M., Fadhil Al Quraishi, A.M., Nangia, V., Zhu, M. and Liu, G., 2018. Soil salinity prediction and mapping by machine learning regression in C entral M esopotamia, Iraq. Land degradation and development, 29(11): 4005-4014.
| ||
|
آمار تعداد مشاهده مقاله: 1,758 تعداد دریافت فایل اصل مقاله: 917 |
||